
import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
    from transformers.models.esm.modeling_esm import (
        ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
        EsmEmbeddings,
        create_position_ids_from_input_ids,
    )




class 			UpperCAmelCase_     :


  '''simple docstring'''







  def __init__(					self     :					List[Any]     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Dict=13     ,				UpperCamelCase__     :					List[str]=7     ,				UpperCamelCase__     :					Any=False     ,				UpperCamelCase__     :					int=True     ,				UpperCamelCase__     :					Optional[int]=False     ,				UpperCamelCase__     :					Any=True     ,				UpperCamelCase__     :					Union[str, Any]=33     ,				UpperCamelCase__     :					Any=32     ,				UpperCamelCase__     :					Dict=5     ,				UpperCamelCase__     :					List[str]=4     ,				UpperCamelCase__     :					List[str]=37     ,				UpperCamelCase__     :					Union[str, Any]="gelu"     ,				UpperCamelCase__     :					List[str]=0.1     ,				UpperCamelCase__     :					Union[str, Any]=0.1     ,				UpperCamelCase__     :					str=512     ,				UpperCamelCase__     :					int=16     ,				UpperCamelCase__     :					Tuple=2     ,				UpperCamelCase__     :					Any=0.02     ,				UpperCamelCase__     :					List[Any]=3     ,				UpperCamelCase__     :					Tuple=4     ,				UpperCamelCase__     :					Optional[Any]=None     ,				)     ->      str:







    """simple docstring"""






    __magic_name__								=			parent
    __magic_name__								=			batch_size
    __magic_name__								=			seq_length
    __magic_name__								=			is_training
    __magic_name__								=			use_input_mask
    __magic_name__								=			use_token_type_ids
    __magic_name__								=			use_labels
    __magic_name__								=			vocab_size
    __magic_name__								=			hidden_size
    __magic_name__								=			num_hidden_layers
    __magic_name__								=			num_attention_heads
    __magic_name__								=			intermediate_size
    __magic_name__								=			hidden_act
    __magic_name__								=			hidden_dropout_prob
    __magic_name__								=			attention_probs_dropout_prob
    __magic_name__								=			max_position_embeddings
    __magic_name__								=			type_vocab_size
    __magic_name__								=			type_sequence_label_size
    __magic_name__								=			initializer_range
    __magic_name__								=			num_labels
    __magic_name__								=			num_choices
    __magic_name__								=			scope
  def 				_lowercase				(					self     :					Dict       )     ->      Any:







    """simple docstring"""






    __magic_name__								=			ids_tensor([self.batch_size, self.seq_length]     ,				self.vocab_size       )

    __magic_name__								=			None
    if self.use_input_mask:
      __magic_name__								=			random_attention_mask([self.batch_size, self.seq_length]       )

    __magic_name__								=			None
    __magic_name__								=			None
    __magic_name__								=			None
    if self.use_labels:
      __magic_name__								=			ids_tensor([self.batch_size]     ,				self.type_sequence_label_size       )
      __magic_name__								=			ids_tensor([self.batch_size, self.seq_length]     ,				self.num_labels       )
      __magic_name__								=			ids_tensor([self.batch_size]     ,				self.num_choices       )

    __magic_name__								=			self.get_config()

    return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
  def 				_lowercase				(					self     :					List[Any]       )     ->      Tuple:







    """simple docstring"""






    return EsmConfig(
        vocab_size=self.vocab_size     ,				hidden_size=self.hidden_size     ,				pad_token_id=1     ,				num_hidden_layers=self.num_hidden_layers     ,				num_attention_heads=self.num_attention_heads     ,				intermediate_size=self.intermediate_size     ,				hidden_act=self.hidden_act     ,				hidden_dropout_prob=self.hidden_dropout_prob     ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,				max_position_embeddings=self.max_position_embeddings     ,				type_vocab_size=self.type_vocab_size     ,				initializer_range=self.initializer_range     ,				)
  def 				_lowercase				(					self     :					Tuple     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					Optional[Any]     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					List[Any]       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			EsmModel(config=UpperCamelCase__       )
    model.to(UpperCamelCase__       )
    model.eval()
    __magic_name__								=			model(UpperCamelCase__     ,				attention_mask=UpperCamelCase__       )
    __magic_name__								=			model(UpperCamelCase__       )
    __magic_name__								=			model(UpperCamelCase__       )

    self.parent.assertEqual(result.last_hidden_state.shape     ,				(self.batch_size, self.seq_length, self.hidden_size)       )
    self.parent.assertEqual(result.pooler_output.shape     ,				(self.batch_size, self.hidden_size)       )
  def 				_lowercase				(					self     :					List[str]     ,				UpperCamelCase__     :					Dict     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					List[Any]     ,				UpperCamelCase__     :					Optional[int]     ,				UpperCamelCase__     :					Optional[int]       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			EsmForMaskedLM(config=UpperCamelCase__       )
    model.to(UpperCamelCase__       )
    model.eval()
    __magic_name__								=			model(UpperCamelCase__     ,				attention_mask=UpperCamelCase__     ,				labels=UpperCamelCase__       )
    self.parent.assertEqual(result.logits.shape     ,				(self.batch_size, self.seq_length, self.vocab_size)       )
  def 				_lowercase				(					self     :					int     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					Optional[Any]     ,				UpperCamelCase__     :					Optional[Any]     ,				UpperCamelCase__     :					List[Any]     ,				UpperCamelCase__     :					Any       )     ->      int:







    """simple docstring"""






    __magic_name__								=			self.num_labels
    __magic_name__								=			EsmForTokenClassification(config=UpperCamelCase__       )
    model.to(UpperCamelCase__       )
    model.eval()
    __magic_name__								=			model(UpperCamelCase__     ,				attention_mask=UpperCamelCase__     ,				labels=UpperCamelCase__       )
    self.parent.assertEqual(result.logits.shape     ,				(self.batch_size, self.seq_length, self.num_labels)       )





  def 				_lowercase				(					self     :					Optional[Any]       )     ->      int:







    """simple docstring"""






    __magic_name__								=			self.prepare_config_and_inputs()
    (
        (
        __magic_name__
    )	,       (
        __magic_name__
    )	,       (
        __magic_name__
    )	,       (
        __magic_name__
    )	,       (
        __magic_name__
    )	,       (
        __magic_name__
    )	,       
    )								=			config_and_inputs
    __magic_name__								=			{"""input_ids""": input_ids, """attention_mask""": input_mask}
    return config, inputs_dict







@require_torch
class 			UpperCAmelCase_     (					_A    ,			_A    ,			unittest.TestCase	):


  '''simple docstring'''







  a__       			=						False

  a__       			=						(
      (
          EsmForMaskedLM,
          EsmModel,
          EsmForSequenceClassification,
          EsmForTokenClassification,
      )
      if is_torch_available()
      else ()
  )
  a__       			=						()
  a__       			=						(
      {
          """feature-extraction""": EsmModel,
          """fill-mask""": EsmForMaskedLM,
          """text-classification""": EsmForSequenceClassification,
          """token-classification""": EsmForTokenClassification,
          """zero-shot""": EsmForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  a__       			=						True
  def 				_lowercase				(					self     :					int       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			EsmModelTester(self       )
    __magic_name__								=			ConfigTester(self     ,				config_class=UpperCamelCase__     ,				hidden_size=37       )
  def 				_lowercase				(					self     :					List[str]       )     ->      List[str]:







    """simple docstring"""






    self.config_tester.run_common_tests()
  def 				_lowercase				(					self     :					Optional[int]       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*UpperCamelCase__       )
  def 				_lowercase				(					self     :					int       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			self.model_tester.prepare_config_and_inputs()
    for type in ["absolute", "relative_key", "relative_key_query"]:
      __magic_name__								=			type
      self.model_tester.create_and_check_model(*UpperCamelCase__       )
  def 				_lowercase				(					self     :					Any       )     ->      int:







    """simple docstring"""






    __magic_name__								=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__       )
  def 				_lowercase				(					self     :					List[Any]       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__       )
  @slow
  def 				_lowercase				(					self     :					Dict       )     ->      Optional[int]:







    """simple docstring"""






    for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      __magic_name__								=			EsmModel.from_pretrained(UpperCamelCase__       )
      self.assertIsNotNone(UpperCamelCase__       )
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			self.model_tester.prepare_config_and_inputs()[0]
    __magic_name__								=			EsmEmbeddings(config=UpperCamelCase__       )

    __magic_name__								=			torch.as_tensor([[12, 31, 13, model.padding_idx]]       )
    __magic_name__								=			torch.as_tensor(
        [
            [
                0 + model.padding_idx + 1,
                1 + model.padding_idx + 1,
                2 + model.padding_idx + 1,
                model.padding_idx,
            ]
        ]       )
    __magic_name__								=			create_position_ids_from_input_ids(UpperCamelCase__     ,				model.padding_idx       )
    self.assertEqual(position_ids.shape     ,				expected_positions.shape       )
    self.assertTrue(torch.all(torch.eq(UpperCamelCase__     ,				UpperCamelCase__       )       )       )
  def 				_lowercase				(					self     :					Tuple       )     ->      List[str]:







    """simple docstring"""






    __magic_name__								=			self.model_tester.prepare_config_and_inputs()[0]
    __magic_name__								=			EsmEmbeddings(config=UpperCamelCase__       )

    __magic_name__								=			torch.empty(2     ,				4     ,				30       )
    __magic_name__								=			[
        0 + embeddings.padding_idx + 1,
        1 + embeddings.padding_idx + 1,
        2 + embeddings.padding_idx + 1,
        3 + embeddings.padding_idx + 1,
    ]
    __magic_name__								=			torch.as_tensor([expected_single_positions, expected_single_positions]       )
    __magic_name__								=			embeddings.create_position_ids_from_inputs_embeds(UpperCamelCase__       )
    self.assertEqual(position_ids.shape     ,				expected_positions.shape       )
    self.assertTrue(torch.all(torch.eq(UpperCamelCase__     ,				UpperCamelCase__       )       )       )
  @unittest.skip("""Esm does not support embedding resizing"""       )
  def 				_lowercase				(					self     :					Union[str, Any]       )     ->      int:







    """simple docstring"""






    pass
  @unittest.skip("""Esm does not support embedding resizing"""       )
  def 				_lowercase				(					self     :					List[str]       )     ->      List[str]:







    """simple docstring"""






    pass





  @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""       )
  def 				_lowercase				(					self     :					List[str]       )     ->      List[Any]:







    """simple docstring"""






    pass







@require_torch
class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  @slow
  def 				_lowercase				(					self     :					Dict       )     ->      Optional[Any]:







    """simple docstring"""






    with torch.no_grad():
      __magic_name__								=			EsmForMaskedLM.from_pretrained("""facebook/esm2_t6_8M_UR50D"""       )
      model.eval()
      __magic_name__								=			torch.tensor([[0, 1, 2, 3, 4, 5]]       )
      __magic_name__								=			model(UpperCamelCase__       )[0]

      __magic_name__								=			33

      __magic_name__								=			torch.Size((1, 6, vocab_size)       )
      self.assertEqual(output.shape     ,				UpperCamelCase__       )

      __magic_name__								=			torch.tensor(
          [[[8.9215, -10.5898, -6.4671], [-6.3967, -13.9114, -1.1212], [-7.7812, -13.9516, -3.7406]]]       )
      self.assertTrue(torch.allclose(output[:, :3, :3]     ,				UpperCamelCase__     ,				atol=1E-4       )       )





  @slow
  def 				_lowercase				(					self     :					int       )     ->      Any:







    """simple docstring"""






    with torch.no_grad():
      __magic_name__								=			EsmModel.from_pretrained("""facebook/esm2_t6_8M_UR50D"""       )
      model.eval()

      __magic_name__								=			torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]       )
      __magic_name__								=			model(UpperCamelCase__       )[0]
      # compare the actual values for a slice.
      __magic_name__								=			torch.tensor(
          [[[0.1444, 0.5413, 0.3248], [0.3034, 0.0053, 0.3108], [0.3228, -0.2499, 0.3415]]]       )
      self.assertTrue(torch.allclose(output[:, :3, :3]     ,				UpperCamelCase__     ,				atol=1E-4       )       )



def a__       (			A_=28123   ):






  '''simple docstring'''





  __magic_name__								=			[1] * (limit + 1)

  for i in range(2,	int(limit**0.5   ) + 1   ):
    sum_divs[i * i] += i
    for k in range(i + 1,	limit // i + 1   ):
      sum_divs[k * i] += k + i

  __magic_name__								=			set()
  __magic_name__								=			0

  for n in range(1,	limit + 1   ):
    if sum_divs[n] > n:
      abundants.add(A_   )

    if not any((n - a in abundants) for a in abundants   ):
      res += n

  return res


if __name__ == "__main__":
    print(solution())

